Python vs R: Which One is Better Suited for Data Science?

10 November 2017

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When contemplating the best programming language to use for data science, Python and R come to mind (almost immediately). While there are plenty of languages like C, C++, Clojure, Java, Julia, Perl, and Scala, it’s safe to say that Python and R are the forerunners in data science.

While a lot of data scientists will talk about the traditional weaknesses like data wrangling in R or data visualization in Python, recent developments like Altair for Python and dplyr for R have effectively responded to these weaknesses.

What’s more, it’s supported by thousands of packages that seamlessly integrate with the following programming languages:

C

C++

Java

More than two decades after it first emerged, R has been adopted widely across industries from Google to Wall Street as a robust alternative to SAS and Matlab. But lately, there has been a significant increase in the adoption of Python by data scientists.

This phenomenon can be attributed to the fact that Python offers a lot of advantages that make it a practical choice for many within the technology industry.

This is supported by Guido van Rossum, the creator of Python, who said "I have this hope that there is a better way. Higher-level tools that actually let you see the structure of the software more clearly will be of tremendous value."

Making the case for Python

Python is known to be quite easy to learn and use because of its readable syntax. It’s also a great language to gain valuable exposure to data science while enhancing your knowledge and experience.

Additionally, Python is a general-purpose programming language, as a result, it can be easily adapted to solve any potential problem. Whether it’s engaging in data mining or building web services, you can utilize Python to solve data related problems from end-to-end.

To identify outliers in a dataset, both Python and R can get the job done efficiently. But if you want to create a web service that enables others to find outliers in the datasets, Python is the best choice.

You can say that Python is also better suited for deep learning (DL). This is because it’s supported by packages like Keras, TensorFlow, and Theano which make the creation of deep neural networks a seamless process.

What’s more, when it comes to supporting DL, Python’s offering is far superior. On top of that, there is also a massive growing community which also includes many from the data science community.

Making the case for R

R is great because packages like CRAN that come with a host of statistical tools and machine learning (ML) algorithms. What’s more, R can be easily extended with C++ with the help of Rcpp.

Just like Scikit-Learn in Python, the Caret package also makes it seamless to use different algorithms within a single interface. What’s more, RStudio provides an excellent independent development environment (IDE).

When it comes to data visualization, R leads the pack with its impressive range of visualization tools like the following:

Choosing one over the other highly depends on the objective of the project. As for Mr. Vladimiro, his objectives are efficiently and effectively addressed by R.

At Intersog, our data scientists believe that it’s all about your comfort zone. So if you’re coming from a computer science background and feel a lot more comfortable working with Python, then that’s the best choice for you.

But if you’re a statistician or a data analyst by trade, R will probably be a more intuitive choice. At Intersog, we love R, but we’re also known to use Python quite a lot.

Are you looking to engage a software and app development provider like Intersog for your next big data project? Click here to schedule a free consultation with one our top data scientists.

Andrew is our IT storyteller and copywriter. His current undertaking is big data analytics and CSS as well as digital design and branding.
He is a contributor to various publications with a focus on new technology and marketing.

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